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Bioinformatics and Computational Biology Graduate Program
Carla Mann
December 11, 2014
Rocky Mountain Bioinformatics Conference
Snowmass, CO
RNABindRPlus Predicts RNA-Protein Interface Residues in Multiple Protein Conformations
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Bioinformatics and ComputationalBiology Graduate Program
RNA-Protein Interactions
• Significance: Implicated in many biological processes beyond transcription/translation
• Why predict?– Hard to crystallize RNA-protein complexes
• Why predict based on sequence instead of structure?
2Bioinformatics and Computational
Biology Graduate Program
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Bioinformatics and ComputationalBiology Graduate Program
RNA-Protein Interaction Prediction:
• 2 Questions:– Interacting partner prediction:
http://pridb.gdcb.iastate.edu/RPISeq/– Interacting residue prediction: http://einstein.cs.iastate.edu/RNABindRPlus/
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> Protein Sequence
SVMOpt: Optimized Support Vector Machine (SVM) classifier-Position Specific Scoring Matrix
(PSSM)
Logistic Regression
Interacting Residue
Prediction
HomPrip: sequence homology-based predictor
RNABindRPlus
Bioinformatics and ComputationalBiology Graduate Program
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Bioinformatics and ComputationalBiology Graduate Program
RNABindRPlus False Positive Predictions Are Not Always False
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T. thermophilus 30S ribosomal protein S2 sequence (aa 1-187 of 256)
Bioinformatics and ComputationalBiology Graduate Program
PDB 2VQE_B 30S ribosomal protein S2 sequenceInterfacial residues are bold
PDB 2VQE_B sequenceInterfacial residues are boldFP highlightedTP boldFN underlined
Combined S2 interfacial residuesInterfacial residues are boldFP highlightedTP boldFN underlined
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Bioinformatics and ComputationalBiology Graduate Program
RNABindRPlus Statistics
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Specificity = TPTP+FP
Sensitivity = TPTP+FN
MCC= (TP x TN )−( FP x FN )
√ (TP+FP ) (TP+FN ) (TN+FP )( TN+FN )
TP TN FP FN Specificity Sensitivity MCC
Single S2 structure (2VQE) 21 219 14 2 0.60 0.91 0.71
Mean for 34 different single S2 structures 23 218 12 3 0.65 0.88 0.73
Combined interfaces from 34 different S2 structures 30 208 5 13 0.86 0.70 0.73
Definitions from: Baldi and Brunak, 2001. Bioinformatics: The Machine Learning Approach
Bioinformatics and ComputationalBiology Graduate Program
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Bioinformatics and ComputationalBiology Graduate Program
Acknowledgements and Further Reading• Walia RR, Xue LC, Wilkins K, El-Manzalawy Y, Dobbs D,
Honavar V. RNABindRPlus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteins. PLoS One. 2014 May 20;9(5):e97725.
• http://einstein.cs.iastate.edu/RNABindRPlus/• John Hsieh’s Poster (P25)• My Poster (P33)
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